Conference item
Partially observable stochastic games with neural perception mechanisms
- Abstract:
- Stochastic games are a well established model for multi-agent sequential decision making under uncertainty. In practical applications, though, agents often have only partial observability of their environment. Furthermore, agents increasingly perceive their environment using data-driven approaches such as neural networks trained on continuous data. We propose the model of neuro-symbolic partially-observable stochastic games (NS-POSGs), a variant of continuous-space concurrent stochastic games that explicitly incorporates neural perception mechanisms. We focus on a one-sided setting with a partially-informed agent using discrete, data-driven observations and another, fully-informed agent. We present a new method, called one-sided NS-HSVI, for approximate solution of one-sided NS-POSGs, which exploits the piecewise constant structure of the model. Using neural network pre-image analysis to construct finite polyhedral representations and particle-based representations for beliefs, we implement our approach and illustrate its practical applicability to the analysis of pedestrian-vehicle and pursuit-evasion scenarios.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 1.8MB, Terms of use)
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- Publisher copy:
- 10.1007/978-3-031-71162-6_19
Authors
- Publisher:
- Springer
- Host title:
- Formal Methods. FM 2024
- Pages:
- 363–380
- Series:
- Lecture Notes in Computer Science
- Series number:
- 14933
- Publication date:
- 2024-09-11
- Acceptance date:
- 2024-07-10
- Event title:
- 26th International Symposium on Formal Methods (FM'24)
- Event location:
- Milan
- Event website:
- https://www.fm24.polimi.it/
- Event start date:
- 2024-09-09
- Event end date:
- 2024-09-13
- DOI:
- EISSN:
-
1611-3349
- ISSN:
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0302-9743
- EISBN:
- 978-3-031-71162-6
- ISBN:
- 978-3-031-71161-9
- Language:
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English
- Pubs id:
-
2011377
- Local pid:
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pubs:2011377
- Deposit date:
-
2024-07-01
Terms of use
- Copyright holder:
- Yan et al.
- Copyright date:
- 2025
- Rights statement:
- © 2025 The Author(s). This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
- Licence:
- CC Attribution (CC BY)
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